CASIA OpenIR  > 09年以前成果
Recursive support vector machines for dimensionality reduction
Tao, Qing1,2; Chu, Dejun2; Wang, Jue1
Source PublicationIEEE TRANSACTIONS ON NEURAL NETWORKS
2008
Volume19Issue:1Pages:189-193
SubtypeArticle
AbstractThe usual dimensionality reduction technique in supervised learning is mainly based on linear discriminant analysis (LDA), but it suffers from singularity or undersampled problems. On the other hand, a regular support vector machine (SVM) separates the data only in terms of one single direction of maximum margin, and the classification accuracy may be not good enough. In this letter, a recursive SVM (RSVM) is presented, in which several orthogonal directions that best separate the data with the maximum margin are obtained. Theoretical analysis shows that a completely orthogonal basis can be derived in feature subspace spanned by the training samples and the margin is decreasing along the recursive components in linearly separable cases. As a result, a new dimensionality reduction technique based on multilevel maximum margin components and then a classifier with high accuracy are achieved. Experiments in synthetic and several real data sets show that RSVM using multilevel maximum margin features can do efficient dimensionality reduction and outperform regular SVM in binary classification problems.
KeywordClassification Dimensionality Reduction Feature Extraction Projection Recursive Support Vector Machines (Rsvms) Support Vector Machines (Svms).
WOS HeadingsScience & Technology ; Technology
WOS KeywordFISHER LINEAR DISCRIMINANT ; FACE RECOGNITION ; THEORETICAL-ANALYSIS
Indexed BySCI
Language英语
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS IDWOS:000252516700017
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/9639
Collection09年以前成果
Affiliation1.Chinese Acad Sci, Inst Automat, Key Lab Complex Syst & Intelligence Sci, Beijing 100080, Peoples R China
2.New Star Res Inst Appl Technol, Hefei 230031, Peoples R China
Recommended Citation
GB/T 7714
Tao, Qing,Chu, Dejun,Wang, Jue. Recursive support vector machines for dimensionality reduction[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS,2008,19(1):189-193.
APA Tao, Qing,Chu, Dejun,&Wang, Jue.(2008).Recursive support vector machines for dimensionality reduction.IEEE TRANSACTIONS ON NEURAL NETWORKS,19(1),189-193.
MLA Tao, Qing,et al."Recursive support vector machines for dimensionality reduction".IEEE TRANSACTIONS ON NEURAL NETWORKS 19.1(2008):189-193.
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